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Introduction

Chapter 1: Zero-shot Learning for Image Captioning
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Zero-shot Learning
2.2 Image Captioning Techniques
2.3 Zero-shot Learning for Image Captioning
2.4 Challenges in Zero-shot Learning for Image Captioning
2.5 Existing Approaches in Zero-shot Learning for Image Captioning
2.6 Evaluation Metrics for Image Captioning
2.7 Transfer Learning in Image Captioning
2.8 Semantic Embeddings for Zero-shot Learning
2.9 Neural Network Architectures for Image Captioning
2.10 Conclusion of Literature Review

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Preprocessing of Images and Captions
3.3 Feature Extraction
3.4 Zero-shot Learning Model
3.5 Training and Testing Procedures
3.6 Evaluation Metrics
3.7 Experiment Design
3.8 Ethical Considerations in Data Collection
3.9 Limitations of Research Methodology

Chapter 4: Discussion of Findings
4.1 Performance Evaluation of Zero-shot Learning Model
4.2 Comparison with Existing Approaches
4.3 Analysis of Results
4.4 Interpretation of Findings
4.5 Implications of Findings
4.6 Future Research Directions
4.7 Challenges Faced During Research
4.8 Recommendations for Improvement

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contribution to Knowledge
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Conclusion
5.6 Future Research Directions

Thesis Overview on Zero-shot Learning for Image Captioning

Zero-shot learning for image captioning is a challenging task that involves generating textual descriptions for images without relying on a pre-defined vocabulary. This thesis explores the use of zero-shot learning techniques in the context of image captioning, where the models are trained to describe images without seeing any examples of the target classes during training.

Chapter 1 provides an introduction to the research topic, discussing the background of study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive review of the literature on zero-shot learning, image captioning techniques, challenges, existing approaches, evaluation metrics, transfer learning, semantic embeddings, and neural network architectures.

Chapter 3 details the research methodology, including data collection, preprocessing, feature extraction, model development, training, testing, evaluation metrics, experiment design, and ethical considerations. Chapter 4 discusses the findings of the research, including performance evaluation, comparisons with existing approaches, analysis of results, implications, and recommendations.

Chapter 5 concludes the thesis with a summary of findings, contribution to knowledge, practical implications, limitations, conclusions, and suggestions for future research directions. Overall, this thesis aims to advance the field of zero-shot learning for image captioning and contribute new insights to the research community.

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